tensorboard

How do I use the Tensorboard callback of Keras?

由 烈酒焚心 提交于 2019-11-27 16:40:47
I have built a neural network with Keras. I would visualize its data by Tensorboard, therefore I have utilized: keras.callbacks.TensorBoard(log_dir='/Graph', histogram_freq=0, write_graph=True, write_images=True) as explained in keras.io . When I run the callback I get <keras.callbacks.TensorBoard at 0x7f9abb3898> , but I don't get any file in my folder "Graph". Is there something wrong in how I have used this callback? Nassim Ben keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True) This line creates a Callback Tensorboard object, you should

Keras - Save image embedding of the mnist data set

由 谁说我不能喝 提交于 2019-11-27 15:03:26
问题 I've written the following simple MLP network for the MNIST db. from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout from keras import callbacks batch_size = 100 num_classes = 10 epochs = 20 tb = callbacks.TensorBoard(log_dir='/Users/shlomi.shwartz/tensorflow/notebooks/logs/minist', histogram_freq=10, batch_size=32, write_graph=True, write_grads=True, write_images=True, embeddings_freq

Understanding TensorBoard (weight) histograms

由 邮差的信 提交于 2019-11-27 10:04:25
It is really straightforward to see and understand the scalar values in TensorBoard. However, it's not clear how to understand histogram graphs. For example, they are the histograms of my network weights. (After fixing a bug thanks to sunside) What is the best way to interpret these? Layer 1 weights look mostly flat, what does this mean? I added the network construction code here. X = tf.placeholder(tf.float32, [None, input_size], name="input_x") x_image = tf.reshape(X, [-1, 6, 10, 1]) tf.summary.image('input', x_image, 4) # First layer of weights with tf.name_scope("layer1"): W1 = tf.get

TensorFlow - Importing data from a TensorBoard TFEvent file?

由 与世无争的帅哥 提交于 2019-11-27 09:40:45
问题 I've run several training sessions with different graphs in TensorFlow. The summaries I set up show interesting results in the training and validation. Now, I'd like to take the data I've saved in the summary logs and perform some statistical analysis and in general plot and look at the summary data in different ways. Is there any existing way to easily access this data? More specifically, is there any built in way to read a TFEvent record back into Python? If there is no simple way to do

Simple way to visualize a TensorFlow graph in Jupyter?

由 馋奶兔 提交于 2019-11-27 05:54:50
The official way to visualize a TensorFlow graph is with TensorBoard, but sometimes I just want a quick look at the graph when I'm working in Jupyter. Is there a quick solution, ideally based on TensorFlow tools, or standard SciPy packages (like matplotlib), but if necessary based on 3rd party libraries? TensorFlow 2.0 now supports TensorBoard in Jupyter via magic commands (e.g %tensorboard --logdir logs/train ). Here's a link to tutorials and examples. [EDITS 1, 2] As @MiniQuark mentioned in a comment, we need to load the extension first( %load_ext tensorboard.notebook ). Below are usage

Tensorflow Confusion Matrix in TensorBoard

由 假装没事ソ 提交于 2019-11-27 05:22:30
问题 I want to have a visual of confusion matrix in tensorboard. To do this, I am modifying Evaluation example of Tensorflow Slim: https://github.com/tensorflow/models/blob/master/slim/eval_image_classifier.py In this example code, Accuracy already provided but it is not possible to add "confusion matrix" metric directly because it is not streaming. What is difference between streaming metrics and non-streaming ones? Therefore, I tried to add it like this: c_matrix = slim.metrics.confusion_matrix

Logging training and validation loss in tensorboard

由 浪尽此生 提交于 2019-11-27 05:15:35
问题 I'm trying to learn how to use tensorflow and tensorboard. I have a test project based on the MNIST neural net tutorial. In my code, I construct a node that calculates the fraction of digits in a data set that are correctly classified, like this: correct = tf.nn.in_top_k(self._logits, labels, 1) correct = tf.to_float(correct) accuracy = tf.reduce_mean(correct) Here, self._logits is the inference part of the graph, and labels is a placeholder that contains the correct labels. Now, what I would

Show training and validation accuracy in TensorFlow using same graph

由 。_饼干妹妹 提交于 2019-11-27 05:13:42
问题 I have a TensorFlow model, and one part of this model evaluates the accuracy. The accuracy is just another node in the tensorflow graph, that takes in logits and labels . When I want to plot the training accuracy, this is simple: I have something like: tf.scalar_summary("Training Accuracy", accuracy) tf.scalar_summary("SomethingElse", foo) summary_op = tf.merge_all_summaries() writer = tf.train.SummaryWriter('/me/mydir/', graph=sess.graph) Then, during my training loop, I have something like:

How do display different runs in TensorBoard?

由 我们两清 提交于 2019-11-27 04:17:13
问题 TensorBoard seems to have a feature to display multiple different runs and toggle them. How can I make multiple runs show up here and how can assign a name to them to differentiate them? 回答1: In addition to TensorBoard scanning subdirectories (so you can pass a directory containing the directories with your runs), you can also pass multiple directories to TensorBoard explicitly and give custom names (example taken from the --help output): tensorboard --logdir=name1:/path/to/logs/1,name2:/path

Is gradient in the tensorflow's graph calculated incorrectly?

由 旧城冷巷雨未停 提交于 2019-11-27 00:58:28
问题 A very simple example in tensorflow: min (x + 1)^2 where x is a scalar. The code is: import tensorflow as tf x = tf.Variable(initial_value=3.0) add = tf.add(x, 1) y = tf.square(add) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) train = optimizer.minimize(y) then write graph to disk graph = tf.get_default_graph() writer = tf.summary.FileWriter("some/dir/to/write/events") writer.add_graph(graph=graph) finally visualize it in tensorboard, it looks like this question is, why
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